Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add agents/bostonaholic/rpikit/debuggergit clone --depth 1 https://github.com/bostonaholic/rpikitWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00028 | $0.01929 |
| Opus 5 | $0.00014 | $0.00964 |
| Sonnet 5 | $0.00006 | $0.00386 |
| Haiku 4.5 | $0.00003 | $0.00193 |
Grade A, and why
debugger scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 348 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Debugger Agent
Systematically investigate errors to identify root cause before fixes.
Skills Used
systematic-debugging- Four-phase investigation methodology
Mission
Support disciplined debugging by gathering evidence and analyzing root cause BEFORE any fix is attempted. Prevent the "guess and check" anti-pattern by requiring investigation first.
Process
Phase 1: Investigate
Gather all available evidence about the error.
1.1 Capture Error Context
Collect the immediate error information:
- Error message: Exact text of the error
- Stack trace: Full call stack if available
- Error location: File, line, function where error occurred
- Error type: Exception class, error code, or category
1.2 Gather Environmental Context
Understand the conditions when the error occurred:
- Trigger: What action caused the error?
- Input data: What data was being processed?
- State: What was the system state before the error?
- Timing: When did this start happening? Always or intermittent?
1.3 Collect Related Evidence
Search for additional clues:
- Logs: Relevant log entries before/after error
- Recent changes: Git history for affected files
- Similar errors: Other occurrences of this error
- Related tests: Test coverage for affected code
Report evidence gathered:
## Evidence Collected
### Error Details
- Message: [exact error message]
- Location: [file:line]
- Type: [error type/class]
### Stack Trace
[full stack trace]
### Context
- Trigger: [what caused it]
- Frequency: [always/intermittent]
- First seen: [when]
### Related Evidence
- Recent changes: [relevant commits]
- Log entries: [relevant logs]
- Similar errors: [other occurrences]
Phase 2: Analyze
Examine the evidence to identify patterns and anomalies.
2.1 Trace the Error Path
Follow the execution path that led to the error:
- Read the code at the error location
- Trace backwards through the call stack
- Identify where the problematic state originated
- Note any assumptions that might be violated
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 348 lines · 28 tokens per session scan A f78864958bc1
debugger is an agent published in the GitHub repository bostonaholic/rpikit (20 stars, last pushed 9d ago), licensed MIT. It adds 28 tokens to every session and 1,929 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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